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Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.


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Proceedings Article
09 Aug 2003
TL;DR: A new LSA algorithm significantly improves the precision of AutoTutor's natural language understanding and can be applied to othernatural language understanding applications.
Abstract: The intelligent tutoring system AutoTutor uses latent semantic analysis to evaluate student answers to the tutor's questions. By comparing a student's answer to a set of expected answers, the system determines how much information is covered and how to continue the tutorial. Despite the success of LSA in tutoring conversations, the system sometimes has difficulties determining at an early stage whether or not an expectation is covered. A new LSA algorithm significantly improves the precision of AutoTutor's natural language understanding and can be applied to other natural language understanding applications.

16 citations

Proceedings Article
01 Jan 2014
TL;DR: Results revealed a positive relation between deterministic behavior patterns and daily performance measures and indicated that students’ propensity to interact in a controlled manner varied as a function of their commitment to learning.
Abstract: The authors use dynamical analyses to investigate the relation between students’ patterns of interactions with various types of game-based features and their daily performance. High school students (n=40) interacted with a game-based intelligent tutoring system across eight sessions. Hurst exponents were calculated based on students’ choice of interactions with four types of gamebased features: generative practice, identification mini-games, personalizable features, and achievement screens. These exponents indicate the extent to which students’ interaction patterns with game-based features are random or deterministic (i.e., controlled). Results revealed a positive relation between deterministic behavior patterns and daily performance measures. Further analyses indicated that students’ propensity to interact in a controlled manner varied as a function of their commitment to learning. Overall, these results provide insight into the potential relations between students’ pattern of choices, individual differences in learning commitment, and daily performance in a learning environment.

16 citations

Proceedings ArticleDOI
27 Jun 2011
TL;DR: A new method which uses a fuzzy classification tree to build a fuzzy predictive model using variables which are captured through natural language dialogue is proposed and early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables.
Abstract: Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual's learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables.

16 citations

Journal ArticleDOI
TL;DR: The generic design philosophy of Byzantium and its associated intelligent tutoring tools are described, together with commentary that places Byzantine in the tradition of the adaptive teaching machines and conversational tutorial systems developed by Gordon Pask.
Abstract: Describes Byzantium, an intelligent tutoring system for teaching the concepts and skills of accounting. The generic design philosophy of Byzantium and its associated intelligent tutoring tools are described, together with commentary that places Byzantium in the tradition of the adaptive teaching machines and conversational tutorial systems (SAKI and CASTE) developed by Gordon Pask.

16 citations

01 Sep 1995
TL;DR: After interviews with CST's two expert human tutors and an analysis their human tutoring transcripts, it is concluded that an ITS's student model can provide answers to two important questions: what should be tutoring and how should it be tutored.
Abstract: CIRCSIM-Tutor (CST) is an Intelligent Tutoring System (ITS) designed to assist first year medical students in reasoning about disturbances to blood pressure. Specifically, they must be able to predict the qualitative changes that occur in the human body when it encounters a perturbation. Many of the physiological concepts used to accurately make predictions are counter intuitive and, therefore, this domain is ideal for studying the dynamics of tutoring. A study of human tutoring sessions is the basis for the design of CST. The student model is the tutor's (ITS or human) assessment of the student's cognitive state. After interviews with CST's two expert human tutors and an analysis their human tutoring transcripts, I have concluded that an ITS's student model can provide answers to two important questions: what should be tutored and how should it be tutored. In CST, the tutor selects an error pattern, a set of student prediction errors that violate a physiological concept. Then, through natural language dialogue, CST attempts to guide the student into an understanding of that physiological concept. A study of these two expert human tutors reveals that, while they employ many tactics to achieve this end, they virtually always try hinting first. A hint is an utterance that is intended to assist the student without providing the answer. Hints are occasionally mentioned in the ITS literature but there has been no systematic study of this phenomenon. I have identified two major categories of hints: (1) hints that convey information (CI-Hints) and (2) hints that point to information (PT-Hints). An analysis of the use of hints by these two tutor's and of the corresponding student responses reveals consistent patterns regarding when and how to hint. The tutor maintains a global assessment (how well is the student's total performance?) and a local assessment (how is the student doing on this topic?). The behavior of these two tutors is the basis for the rules that determine when and how CST hints.

16 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202322
202244
202199
2020110
2019138
2018165